In one embodiment, a method includes receiving instructions to render a snapshot of a scene for a video, where the snapshot is to be displayed using a sequence of N frames, computing a mipmap-level determining factor for a texture appearing in the scene based on a scale of the texture on a pixel grid, selecting a mipmap level of the texture for each of the N frames based on the mipmap-level determining factor, where the mipmap levels selected for the N frames are non-uniform and temporally approximate the mipmap-level determining factor, rendering each of the N frames by sampling the mipmap level of the texture selected for that frame, and displaying the rendered N frames sequentially to represent the snapshot of the scene.

Techniques for rendering two-dimensional vector graphics are described. The techniques include using a central processing unit to generate tessellate triangles along a vector path in which each of the tessellate triangles is represented by a set of vertices. From the tessellate triangles, an index buffer and a compressed vertex buffer are generated. The index buffer includes a vertex index for each vertex of each of the tessellate triangles. The compressed vertex buffer includes a vertex buffer entry for each unique vertex that maps to one or more vertex indices of the index buffer. The index buffer and the compressed vertex buffer are provided to a graphics processing unit to render the vector path with anti-aliasing.

A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Described herein is a data processing system having a multisample antialiasing compressor coupled to a texture unit and shader execution array. In one embodiment, the data processing system includes a memory device to store a multisample render target, the multisample render target to store color data for a set of sample locations of each pixel in a set of pixels; and general-purpose graphics processor comprising a multisample antialiasing compressor to apply multisample antialiasing compression to color data generated for the set of sample locations of a first pixel in the set of pixels and a multisample render cache to store color data generated for the set of sample locations of the first pixel in the set of pixels, wherein color data evicted from the multisample render cache is to be stored to the multisample render target.

Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.

Appearance driven automatic three-dimensional (3D) modeling enables optimization of a 3D model comprising the shape and appearance of a particular 3D scene or object. Triangle meshes and shading models may be jointly optimized to match the appearance of a reference 3D model based on reference images of the reference 3D model. Compared with the reference 3D model, the optimized 3D model is a lower resolution 3D model that can be rendered in less time. More specifically, the optimized 3D model may include fewer geometric primitives compared with the reference 3D model. In contrast with the conventional inverse rendering or analysis-by-synthesis modeling tools, the shape and appearance representations of the 3D model are automatically generated that, when rendered, match the reference images. Appearance driven automatic 3D modeling has a number of uses, including appearance-preserving simplification of extremely complex assets, conversion between rendering systems, and even conversion between geometric scene representations.

An embodiment of a parallel processor apparatus may include a sample pattern selector to select a sample pattern for a pixel, and a sample pattern subset selector communicatively coupled to the sample pattern selector to select a first subset of the sample pattern for the pixel corresponding to a left eye display frame and to select a second subset of the sample pattern for the pixel corresponding to a right eye display frame, wherein the second subset is different from the first subset. Other embodiments are disclosed and claimed.

One embodiment provides a graphics processor comprising an interface to a system interconnect and a graphics processor coupled to the interface, the graphics processor comprising circuitry configured to compact sample data for multiple sample locations of a pixel, map the multiple sample locations to memory locations that store compacted sample data, the memory locations in a memory of the graphics processor, apply lossless compression to the compacted sample data, and update a compression control surface associated with the memory locations, the compression control surface to specify a compression status for the memory locations

A graphics processor is provided that includes circuitry configured to receive, at an input block of a neural network model, a set of data including previous frame data, current frame data, velocity data, and jitter offset data. The neural network model is configured to generate a denoised, supersampled, and anti-aliased output image based on reliability metrics computed based on sample distribution data for samples within the current frame data.

Systems, apparatuses and methods may provide away to render edges of an object defined by multiple tessellation triangles. More particularly, systems, apparatuses and methods may provide a way to perform anti-aliasing at the edges of the object based on a coarse pixel rate, where the coarse pixels may be based on a coarse Z value indicate a resolution or granularity of detail of the coarse pixel. The systems, apparatuses and methods may use a shader dispatch engine to dispatch raster rules to a pixel shader to direct the pixel shader to include, in a tile and/or tessellation triangle, one more finer coarse pixels based on a percent of coverage provided by a finer coarse pixel of a tessellation triangle at or along the edge of the object.